Research on Fault Diagnosis Method of Rotating Machinery Based on Extreme Learning Machine
Authors
Dequan Yu, Yang Wang, Wenbo Wu, Hongyong Fu, Ke Wang
Corresponding Author
Dequan Yu
Available Online October 2019.
- DOI
- 10.2991/mbdasm-19.2019.13How to use a DOI?
- Keywords
- information entropy; intrinsic mode function; extreme learning machine
- Abstract
The space station has gradually entered the world. It is equipped with centrifuges for variable gravity experiments. The stagnation of centrifuge may lead to the increase of motor current, which may lead to fire. Vibration signal of centrifuge is unstable and asymmetric. Secondly, the first order modal functions are used to obtain the spectrum by Fourier transform, and the information entropy intrinsic mode function of the spectrum is calculated. At the same time, information entropy is used as a fault feature and dimensionality reduction. Finally, the fault features are trained by the extreme learning machine method, and the actual data acquisition training method is used.
- Copyright
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Dequan Yu AU - Yang Wang AU - Wenbo Wu AU - Hongyong Fu AU - Ke Wang PY - 2019/10 DA - 2019/10 TI - Research on Fault Diagnosis Method of Rotating Machinery Based on Extreme Learning Machine BT - Proceedings of the 2019 International Conference on Mathematics, Big Data Analysis and Simulation and Modelling (MBDASM 2019) PB - Atlantis Press SP - 56 EP - 59 SN - 2352-538X UR - https://doi.org/10.2991/mbdasm-19.2019.13 DO - 10.2991/mbdasm-19.2019.13 ID - Yu2019/10 ER -